On the use of a consumer-grade 360-degree camera as a radiometer for scientific applications
Bibliographic record
Abstract
Improved miniaturization capabilities for complex fisheye camera systems have recently led to the introduction of many compact 360-degree cameras on the consumer technology market. Designed primarily for recreational photography, several manufacturers have decided to allow users access to raw imagery for further editing flexibility, thereby offering data at sensor level that can be directly exploited for absolute-light quantification. In this study, we demonstrate methodologies to carefully calibrate a consumer-grade 360-degree camera for radiometry use. The methods include linearity analysis, geometric calibration, assessment of the illumination fall-off across the image plane, spectral-response determination, absolute spectral-radiance calibration, immersion factor determination and dark-frame analysis. Accuracy of the calibration was validated by a real-world experiment comparing sky radiance measurements with a co-localized Compact Optical Profiling System (C-OPS, Biospherical Instruments Inc.), which gave mean unbiased percentage differences of less than 21.1 %. Using the photon-transfer technique, we calculated that this camera consisting of two fisheyes with a 182$^\circ$ field of view in air (152$^\circ$ in water) has a limit of detection of at least $4.6 \times 10^{-7}$ $\mathrm{W \cdot sr^{-1} \cdot m^{-2} \cdot nm^{-1}}$ in its three spectral channels. This technology, with properly stored calibration data, may benefit researchers from multiple scientific areas interested in radiometric geometric light-field study. While some of these radiometric calibration methods are complex or costly, this work opens up possibilities for easy-to-use, inexpensive, and accessible radiance cameras.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".